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Automatic logo transition detection in digital video contents

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Abstract

The amount of user created contents has been increasing rapidly and is associated with a serious copyright problem. Automatic logo detection and recognition in videos is a natural and efficient way of overcoming the copyright problem. However, logos have varying characteristics, which make logo detection and recognition very difficult. Moreover, logo transitions between two different logos exist in one video comprising several video contents. This disrupts the automatic logo detection and recognition. Therefore, in order to improve logo detection, it is necessary to take into account the logo transitions explicitly. This paper proposes an accurate logo transition detection method for recognizing logos in digital video contents. The proposed method accurately segments a video according to logo and efficiently recognizes various types of logos. The experimental results demonstrate the effectiveness of the proposed method for logo detection and video segmentation according to logo.

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References

  1. Cózar JR, Guil N, Gonzalez-Linares JM, Zapata EL, Izquierdo E (2007) Logotype detection to support semantic-based video annotation. Signal Process Image Commun 22(7):669–679

    Article  Google Scholar 

  2. Yan WQ, Wang J, Kankanhalli M (2005) Yan WQ, Wang J, Kankanhalli M (2005) Automatic video logo detection and removal. Multimed Syst 10(5):379–391

    Article  Google Scholar 

  3. Fang H, Jiang J, Feng Y (2006) A fuzzy logic approach for detection of video shot boundaries. Pattern Recognit 39(11):2092–2100

    Article  MATH  Google Scholar 

  4. Bai H, Hu W, Wang T, Tong X, Liu C, Zhang Y (2006) A novel sports video logo detector based on motion analysis. LNCS Neural Inf Process 4233:448–457

    Article  Google Scholar 

  5. Kleban J, Xie X, Ma WY (2008) Spatial pyramid mining for logo detection in natural scenes. In: Proceedings of IEEE international conference on multimedia and expo, Hannover, Germany, pp. 1077–1080

  6. Bagdanov A, Ballan L, Bertini M, Bimbo AD (2007) Trademark matching and retrieval in sports video databases. In: Proceedings of international workshop on multimedia information retrieval, Augsburg, Germany, pp. 79–86

  7. Gao K, Lin S, Zhang Y, Tang S, Zhang D (2009) Logo detection based on spatial-spectral saliency and partial spatial context. In: Proceedings of IEEE international conference on Multimedia and Expo, New York, USA, pp. 322–329

  8. Wolf C, Jolion JM (2003) Extraction and recognition of artificial text in multimedia documents. Pattern Anal Appl 6(4):309–326

    MathSciNet  Google Scholar 

  9. Byun HR, Roh MC, Kim KC, Choi YW, Lee SW (2002) Scene text extraction in complex images. LNCS Doc Anal Syst 2423:329–340

    Article  Google Scholar 

  10. Albiol A, Fulla MJ, Albiol A, Torres L (2004) Commercials detection using HMMs. In: Proceedings of IEEE international workshop on image analysis for multimedia interactive services, Lisboa, Portugal

  11. Yeh JH, Chen JC, Kuo JH, Wu JL (2005) TV commercial detection in news program videos. In: Proceedings of IEEE international symposium on circuits and systems, Kobe, Japan, vol 5, pp 4594–4597

  12. Albiol A, Fulla MJ, Albiol A, Torres L (2004) Detection of TV commercials. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, Montreal, Canada, vol 3, pp 541–544

  13. Tong X, Lu H, Liu Q, Jin H (2004) Replay detection in broadcasting sports video. In: Proceedings of 3rd international conference on image and graphics, Hong Kong, China, pp 337–340

  14. Wang J, Liu Q, Duan L, Lu H, Xu C (2007) Automatic TV logo detection, tracking and removal in broadcast. LNCS Adv Multimed Model 4352:63–72

    Article  Google Scholar 

  15. Meisinger K, Troeger T, Zeller M, Kaup A (2005) Automatic TV logo removal using statistical logo detection and frequency selective inpainting. In: Proceedings of 13th European signal processing conference, Antalya, Turkey

  16. Yan WQ, Wang J, Kankanhalli MS (2005) Automatic video logo detection and removal. Multimed Syst 10(5):379–391

    Article  Google Scholar 

  17. Cózar JR , Guil N, Gonzalez-Linares JM, Zapata EL (2006) Video cataloging based on robust logotype detection. In: Proceedings of 2006 IEEE international conference on image processing, Atlanta, USA, pp 3217–3220

  18. Seeber B, Yager N, Amin A (2007) Real-time detection of semi-transparent watermarks in decompressed video. In: Proceedings of IEEE workshop on applications of computer vision, Texas, USA, pp 49–54

  19. Duffner S, C. Garcia (2006) A neural scheme for robust detection of transparent logos in TV programs. LNCS Artif Neural Netw 4132:14–23

    Google Scholar 

  20. Boussaid L, Mtibaa A, Abid M, Paindavoine M (2007) A real-time shot cut detector: hardware implementation. Comput Stand Interfaces 29(3):335–342

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant R31-10008.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-713, Korea

    Chang-Yu Lu, Myung-Cheol Roh & Seung-Yeon Kang

  2. Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-713, Korea

    Seong-Whan Lee

Authors
  1. Chang-Yu Lu
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  2. Myung-Cheol Roh
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  3. Seung-Yeon Kang
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  4. Seong-Whan Lee
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Correspondence to Seong-Whan Lee.

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Lu, CY., Roh, MC., Kang, SY. et al. Automatic logo transition detection in digital video contents. Pattern Anal Applic 15, 175–187 (2012). https://doi.org/10.1007/s10044-012-0267-9

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